Reliable Deep Learning and IoT-Based Monitoring System for Secure Computer Numerical Control Machines Against Cyber-Attacks With Experimental Verification

IEEE Access

Matti Lehtonen¹  Karar Mahmoud¹  Mohamed M. F. Darwish²  Viet Q. Vu³  Almoataz Y. Abdelaziz⁴  Meng-Kun Liu⁵  Mahmoud Elsisi⁵  Minh-Quang Tran⁵ 

[1] Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, Espoo, Finland;Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt;Department of Mechanical Engineering, Faculty of International Training, Thai Nguyen University of Technology, Thai Nguyen, Vietnam;Faculty of Engineering and Technology, Future University in Egypt, Cairo, Egypt;Industry 4.0 Implementation Center, Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei, Taiwan;

关键词: Deep learning;    industry 4.0;    Internet of Things;    smart machines;    milling process;   

DOI  :  10.1109/ACCESS.2022.3153471

来源: DOAJ

【 摘 要 】

This paper introduces a new intelligent integration between an IoT platform and deep learning neural network (DNN) algorithm for the online monitoring of computer numerical control (CNC) machines. The proposed infrastructure is utilized for monitoring the cutting process while maintaining the cutting stability of CNC machines in order to ensure effective cutting processes that can help to increase the quality of products. For this purpose, a force sensor is installed in the milling CNC machine center to measure the vibration conditions. Accordingly, an IoT architecture is designed to connect the sensor node and the cloud server to capture the real-time machine’s status via message queue telemetry transport (MQTT) protocol. To classify the different cutting conditions (i.e., stable cutting and unstable cuttings), an improved model of DNN is designed in order to maintain the healthy state of the CNC machine. As a result, the developed deep learning can accurately investigate if the transmitted data of the smart sensor via the internet is real cutting data or fake data caused by cyber-attacks or the inefficient reading of the sensor due to the environment temperature, humidity, and noise signals. The outstanding results are obtained from the proposed approach indicating that deep learning can outperform other traditional machine learning methods for vibration control. The Contact elements for IoT are utilized to display the cutting information on a graphical dashboard and monitor the cutting process in real-time. Experimental verifications are performed to conduct different cutting conditions of slot milling while implementing the proposed deep machine learning and IoT-based monitoring system. Diverse scenarios are presented to verify the effectiveness of the developed system, where it can disconnect immediately to secure the system automatically when detecting the cyber-attack and switch to the backup broker to continue the runtime operation.

【 授权许可】

Unknown